Crowd and profile based communication addresses

ABSTRACT

A system and method are disclosed for sending a message to a select subset of users in a select crowd of users. In one embodiment, a message to be delivered to a subset of users in a select crowd of users is received from a user device of a sending user. In response, one or more users in the crowd are selected as the subset of the users in the crowd to which the message is to be delivered. In one embodiment, the one or more users are selected based on a profile matching process. The message is then sent to the one or more users selected as the subset of the users in the crowd to which the message is to be delivered. Preferably, the message is sent to the one or more users anonymously such that the message does not identify the sending user.

RELATED APPLICATIONS

This application claims the benefit of provisional patent application Ser. No. 61/289,107, filed Dec. 22, 2009, the disclosure of which is hereby incorporated herein by reference in its entirety.

FIELD OF THE DISCLOSURE

The present disclosure relates to delivering a message to a select subset of users in a select crowd of users.

BACKGROUND

Location-aware mobile devices are prolific in today's digital society. As a result, crowd-based applications that identify and track crowds of users are emerging. One exemplary system for identifying and tracking crowds of users is described in U.S. patent application Ser. No. 12/645,532, entitled FORMING CROWDS AND PROVIDING ACCESS TO CROWD DATA IN A MOBILE ENVIRONMENT, which was filed Dec. 23, 2009; U.S. patent application Ser. No. 12/645,539, entitled ANONYMOUS CROWD TRACKING, which was filed Dec. 23, 2009; U.S. patent application Ser. No. 12/645,535, entitled MAINTAINING A HISTORICAL RECORD OF ANONYMIZED USER PROFILE DATA BY LOCATION FOR USERS IN A MOBILE ENVIRONMENT, which was filed Dec. 23, 2009; U.S. patent application Ser. No. 12/645,546, entitled CROWD FORMATION FOR MOBILE DEVICE USERS, which was filed Dec. 23, 2009; U.S. patent application Ser. No. 12/645,556, entitled SERVING A REQUEST FOR DATA FROM A HISTORICAL RECORD OF ANONYMIZED USER PROFILE DATA IN A MOBILE ENVIRONMENT, which was filed Dec. 23, 2009; U.S. patent application Ser. No. 12/645,560, entitled HANDLING CROWD REQUESTS FOR LARGE GEOGRAPHIC AREAS, which was filed Dec. 23, 2009; and U.S. patent application Ser. No. 12/645,544, entitled MODIFYING A USER'S CONTRIBUTION TO AN AGGREGATE PROFILE BASED ON TIME BETWEEN LOCATION UPDATES AND EXTERNAL EVENTS, which was filed Dec. 23, 2009; all of which are hereby incorporated herein by reference in their entireties. With the emergence of such crowd-based applications, there is a need for a system and method that enables communication with users in crowds at desired locations.

SUMMARY

The present disclosure relates to sending a message to a select subset of users in a select crowd of users. In one embodiment, a message to be delivered to a subset of users in a select crowd of users is received from a user device of a sending user. In response, one or more users in the crowd are selected as the subset of the users in the crowd to which the message is to be delivered. In one embodiment, the one or more users are selected based on a profile matching process. For example, a predefined number of users in the crowd having user profiles that most closely match the user profile of the sending user, or a select portion of the user profile of the sending user, may be selected as the subset of the users in the crowd to which the message is to be delivered. The message is then sent to the one or more users selected as the subset of the users in the crowd to which the message is to be delivered. Preferably, the message is sent to the one or more users anonymously such that the message does not identify the sending user.

Those skilled in the art will appreciate the scope of the present disclosure and realize additional aspects thereof after reading the following detailed description of the preferred embodiments in association with the accompanying drawing figures.

BRIEF DESCRIPTION OF THE DRAWING FIGURES

The accompanying drawing figures incorporated in and forming a part of this specification illustrate several aspects of the disclosure, and together with the description serve to explain the principles of the disclosure.

FIG. 1 illustrates a Mobile Aggregate Profile (MAP) system according to one embodiment of the present disclosure;

FIG. 2 is a block diagram of the MAP server of FIG. 1 according to one embodiment of the present disclosure;

FIG. 3 is a block diagram of the MAP client of one of the mobile devices of FIG. 1 according to one embodiment of the present disclosure;

FIG. 4 illustrates the operation of the system of FIG. 1 to provide user profiles and current locations of the users of the mobile devices to the MAP server according to one embodiment of the present disclosure;

FIG. 5 illustrates the operation of the system of FIG. 1 to provide user profiles and current locations of the users of the mobile devices to the MAP server according to another embodiment of the present disclosure;

FIG. 6 is a flow chart for a spatial crowd formation process according to one embodiment of the present disclosure;

FIGS. 7A through 7D graphically illustrate the crowd formation process of FIG. 6 for an exemplary bounding box;

FIGS. 8A through 8D illustrate a flow chart for a spatial crowd formation process according to another embodiment of the present disclosure;

FIGS. 9A through 9D graphically illustrate the crowd formation process of FIGS. 8A through 8D for a scenario where the crowd formation process is triggered by a location update for a user having no old location;

FIGS. 10A through 10F graphically illustrate the crowd formation process of FIGS. 8A through 8D for a scenario where the new and old bounding boxes overlap;

FIGS. 11A through 11E graphically illustrate the crowd formation process of FIGS. 8A through 8D in a scenario where the new and old bounding boxes do not overlap;

FIG. 12 illustrates the operation of the system of FIG. 1 to generate and deliver a message to a select subset of users in a crowd of users according to one embodiment of the present disclosure;

FIG. 13 illustrates a process for selecting a subset of users in a crowd of users to which to send a message according to one embodiment of the present disclosure;

FIGS. 14A through 14F graphically illustrate the process of FIG. 12 according to one exemplary embodiment of the present disclosure;

FIG. 15 is a block diagram of the MAP server of FIG. 1 according to one embodiment of the present disclosure; and

FIG. 16 is a block diagram of one of the mobile devices of FIG. 1 according to one embodiment of the present disclosure.

DETAILED DESCRIPTION

The embodiments set forth below represent the necessary information to enable those skilled in the art to practice the embodiments and illustrate the best mode of practicing the embodiments. Upon reading the following description in light of the accompanying drawing figures, those skilled in the art will understand the concepts of the disclosure and will recognize applications of these concepts not particularly addressed herein. It should be understood that these concepts and applications fall within the scope of the disclosure and the accompanying claims.

FIG. 1 illustrates a Mobile Aggregate Profile (MAP) system 10 (hereinafter “system 10”) that enables a user to send a message to a select subset of users in a select crowd of users according to one embodiment of the present disclosure. Note that the system 10 is exemplary and is not intended to limit the scope of the present disclosure. In this embodiment, the system 10 includes a MAP server 12, one or more profile servers 14, a location server 16, a number of mobile devices 18-1 through 18-N (generally referred to herein collectively as mobile devices 18 and individually as mobile device 18) having associated users 20-1 through 20-N (generally referred to herein collectively as users 20 and individually as user 20), a subscriber device 22 having an associated subscriber 24, and a third-party service 26 communicatively coupled via a network 28. The network 28 may be any type of network or any combination of networks. Specifically, the network 28 may include wired components, wireless components, or both wired and wireless components. In one exemplary embodiment, the network 28 is a distributed public network such as the Internet, where the mobile devices 18 are enabled to connect to the network 28 via local wireless connections (e.g., WiFi® or IEEE 802.11 connections) or wireless telecommunications connections (e.g., 3G or 4G telecommunications connections such as GSM, LTE, W-CDMA, or WiMAX® connections).

As discussed below in detail, the MAP server 12 operates to obtain current locations, including location updates, and user profiles of the users 20 of the mobile devices 18. The current locations of the users 20 can be expressed as positional geographic coordinates such as latitude-longitude pairs, and a height vector (if applicable), or any other similar information capable of identifying a given physical point in space in a two-dimensional or three-dimensional coordinate system. Using the current locations and user profiles of the users 20, the MAP server 12 is enabled to provide a number of features such as, but not limited to, forming crowds of users using current locations and/or user profiles of the users 20, generating aggregate profiles for crowds of users, and tracking crowds. Note that while the MAP server 12 is illustrated as a single server for simplicity and ease of discussion, it should be appreciated that the MAP server 12 may be implemented as a single physical server or multiple physical servers operating in a collaborative manner for purposes of redundancy and/or load sharing.

In general, the one or more profile servers 14 operate to store user profiles for a number of persons including the users 20 of the mobile devices 18. For example, the one or more profile servers 14 may be servers providing social network services such as the Facebook® social networking service, the MySpace® social networking service, the LinkedIN® social networking service, or the like. As discussed below, using the one or more profile servers 14, the MAP server 12 is enabled to directly or indirectly obtain the user profiles of the users 20 of the mobile devices 18. The location server 16 generally operates to receive location updates from the mobile devices 18 and make the location updates available to entities such as, for instance, the MAP server 12. In one exemplary embodiment, the location server 16 is a server operating to provide Yahoo!'s FireEagle service.

The mobile devices 18 may be mobile smart phones, portable media player devices, mobile gaming devices, or the like. Some exemplary mobile devices that may be programmed or otherwise configured to operate as the mobile devices 18 are the Apple® iPhone®, the Palm Pre®, the Samsung Rogue™, the Blackberry Storm™, the Motorola Droid or similar phone running Google's Android™ Operating System, an Apple® iPad™, and the Apple® iPod Touch® device. However, this list of exemplary mobile devices is not exhaustive and is not intended to limit the scope of the present disclosure.

The mobile devices 18-1 through 18-N include MAP clients 30-1 through 30-N (generally referred to herein collectively as MAP clients 30 or individually as MAP client 30), MAP applications 32-1 through 32-N (generally referred to herein collectively as MAP applications 32 or individually as MAP application 32), third-party applications 34-1 through 34-N (generally referred to herein collectively as third-party applications 34 or individually as third-party application 34), and location functions 36-1 through 36-N (generally referred to herein collectively as location functions 36 or individually as location function 36), respectively. The MAP client 30 is preferably implemented in software. In general, in the preferred embodiment, the MAP client 30 is a middleware layer operating to interface an application layer (i.e., the MAP application 32 and the third-party applications 34) to the MAP server 12. More specifically, the MAP client 30 enables the MAP application 32 and the third-party applications 34 to request and receive data from the MAP server 12. In addition, the MAP client 30 enables applications, such as the MAP application 32 and the third-party applications 34, to access data from the MAP server 12.

The MAP application 32 is also preferably implemented in software. The MAP application 32 generally provides a user interface component between the user 20 and the MAP server 12. More specifically, among other things, the MAP application 32 enables the user 20 to initiate requests for crowd data from the MAP server 12 and present corresponding crowd data returned by the MAP server 12 to the user 20 as well as enable the user 20 to send and receive messages as described below in detail. The MAP application 32 also enables the user 20 to configure various settings. For example, the MAP application 32 may enable the user 20 to select a desired social networking service (e.g., Facebook®, MySpace®, LinkedIN®, etc.) from which to obtain the user profile of the user 20 and provide any necessary credentials (e.g., username and password) needed to access the user profile from the social networking service.

The third-party applications 34 are preferably implemented in software. The third-party applications 34 operate to access the MAP server 12 via the MAP client 30. The third-party applications 34 may utilize data obtained from the MAP server 12 in any desired manner. As an example, one of the third-party applications 34 may be a gaming application that utilizes crowd data to notify the user 20 of Points of Interest (POIs) or Areas of Interest (AOIs) where crowds of interest are currently located. It should be noted that while the MAP client 30 is illustrated as being separate from the MAP application 32 and the third-party applications 34, the present disclosure is not limited thereto. The functionality of the MAP client 30 may alternatively be incorporated into the MAP application 32 and the third-party applications 34.

The location function 36 may be implemented in hardware, software, or a combination thereof. In general, the location function 36 operates to determine or otherwise obtain the location of the mobile device 18. For example, the location function 36 may be or include a Global Positioning System (GPS) receiver. In addition or alternatively, the location function 36 may include hardware and/or software that enables improved location tracking in indoor environments such as, for example, shopping malls. For example, the location function 36 may be part of or compatible with the InvisiTrack Location System provided by InvisiTrack and described in U.S. Pat. No. 7,423,580 entitled “Method and System of Three-Dimensional Positional Finding” which issued on Sep. 9, 2008, U.S. Pat. No. 7,787,886 entitled “System and Method for Locating a Target using RFID” which issued on Aug. 31, 2010, and U.S. Patent Application Publication No. 2007/0075898 entitled “Method and System for Positional Finding Using RF, Continuous and/or Combined Movement” which published on Apr. 5, 2007, all of which are hereby incorporated herein by reference for their teachings regarding location tracking.

The subscriber device 22 is a physical device such as a personal computer, a mobile computer (e.g., a notebook computer, a netbook computer, a tablet computer, etc.), a mobile smart phone, or the like. The subscriber 24 associated with the subscriber device 22 is a person or entity. In general, the subscriber device 22 enables the subscriber 24 to access the MAP server 12 via a web browser 38 to obtain various types of data, preferably for a fee. For example, the subscriber 24 may pay a fee to have access to crowd data such as aggregate profiles for crowds located at one or more POIs and/or located in one or more AOIs, pay a fee to track crowds, or the like. Note that the web browser 38 is exemplary. In another embodiment, the subscriber device 22 is enabled to access the MAP server 12 via a custom application.

Lastly, the third-party service 26 is a service that has access to data from the MAP server 12 such as aggregate profiles for one or more crowds at one or more POIs or within one or more AOIs. Based on the data from the MAP server 12, the third-party service 26 operates to provide a service such as, for example, targeted advertising. For example, the third-party service 26 may obtain anonymous aggregate profile data for one or more crowds located at a POI and then provide targeted advertising to known users located at the POI based on the anonymous aggregate profile data. Note that while targeted advertising is mentioned as an exemplary third-party service 26, other types of third-party services 26 may additionally or alternatively be provided. Other types of third-party services 26 that may be provided will be apparent to one of ordinary skill in the art upon reading this disclosure.

Before proceeding, it should be noted that while the system 10 of FIG. 1 illustrates an embodiment where the one or more profile servers 14 and the location server 16 are separate from the MAP server 12, the present disclosure is not limited thereto. In an alternative embodiment, the functionality of the one or more profile servers 14 and/or the location server 16 may be implemented within the MAP server 12.

FIG. 2 is a block diagram of the MAP server 12 of FIG. 1 according to one embodiment of the present disclosure. As illustrated, the MAP server 12 includes an application layer 40, a business logic layer 42, and a persistence layer 44. The application layer 40 includes a user web application 46, a mobile client/server protocol component 48, and one or more data Application Programming Interfaces (APIs) 50. The user web application 46 is preferably implemented in software and operates to provide a web interface for users, such as the subscriber 24, to access the MAP server 12 via a web browser. The mobile client/server protocol component 48 is preferably implemented in software and operates to provide an interface between the MAP server 12 and the MAP clients 30 hosted by the mobile devices 18. The data APIs 50 enable third-party services, such as the third-party service 26, to access the MAP server 12.

The business logic layer 42 includes a profile manager 52, a location manager 54, a history manager 56, a crowd analyzer 58, an aggregation engine 60, and a message delivery function 62 each of which is preferably implemented in software. The profile manager 52 generally operates to obtain the user profiles of the users 20 directly or indirectly from the one or more profile servers 14 and store the user profiles in the persistence layer 44. The location manager 54 operates to obtain the current locations of the users 20 including location updates. As discussed below, the current locations of the users 20 may be obtained directly from the mobile devices 18 and/or obtained from the location server 16.

The history manager 56 generally operates to maintain a historical record of anonymized user profile data by location. Note that while the user profile data stored in the historical record is preferably anonymized, it is not limited thereto. The crowd analyzer 58 operates to form crowds of users. In one embodiment, the crowd analyzer 58 utilizes a spatial crowd formation algorithm. However, the present disclosure is not limited thereto. In addition, the crowd analyzer 58 may further characterize crowds to reflect degree of fragmentation, best-case and worst-case degree of separation (DOS), and/or degree of bi-directionality. Still further, the crowd analyzer 58 may also operate to track crowds. The aggregation engine 60 generally operates to provide aggregate profile data in response to requests from the mobile devices 18, the subscriber device 22, and the third-party service 26. The aggregate profile data may be historical aggregate profile data for one or more POIs or one or more AOIs or aggregate profile data for crowd(s) currently at one or more POIs or within one or more AOIs. For additional information regarding the operation of the profile manager 52, the location manager 54, the history manager 56, the crowd analyzer 58, and the aggregation engine 60, the interested reader is directed to U.S. patent application Ser. No. 12/645,532, entitled FORMING CROWDS AND PROVIDING ACCESS TO CROWD DATA IN A MOBILE ENVIRONMENT, which was filed Dec. 23, 2009; U.S. patent application Ser. No. 12/645,539, entitled ANONYMOUS CROWD TRACKING, which was filed Dec. 23, 2009; U.S. patent application Ser. No. 12/645,535, entitled MAINTAINING A HISTORICAL RECORD OF ANONYMIZED USER PROFILE DATA BY LOCATION FOR USERS IN A MOBILE ENVIRONMENT, which was filed Dec. 23, 2009; U.S. patent application Ser. No. 12/645,546, entitled CROWD FORMATION FOR MOBILE DEVICE USERS, which was filed Dec. 23, 2009; U.S. patent application Ser. No. 12/645,556, entitled SERVING A REQUEST FOR DATA FROM A HISTORICAL RECORD OF ANONYMIZED USER PROFILE DATA IN A MOBILE ENVIRONMENT, which was filed Dec. 23, 2009; U.S. patent application Ser. No. 12/645,560, entitled HANDLING CROWD REQUESTS FOR LARGE GEOGRAPHIC AREAS, which was filed Dec. 23, 2009; and U.S. patent application Ser. No. 12/645,544, entitled MODIFYING A USER'S CONTRIBUTION TO AN AGGREGATE PROFILE BASED ON TIME BETWEEN LOCATION UPDATES AND EXTERNAL EVENTS, which was filed Dec. 23, 2009; all of which have been incorporated herein by reference in their entireties.

As discussed below in detail, the message delivery function 62 enables delivery, or sending, of messages to select subsets of users in select crowds. In general, a user such as, but not limited to, one of the users 20 selects a desired crowd and defines a message to be delivered to a subset of the users 20 in the desired crowd. The message delivery function 62 selects one or more of the users 20 in the desired crowd as the subset of users 20 in the desired crowd to which the message is to be delivered. Preferably, the one or more users 20 are selected based on profile matching and, optionally, one or more ratings assigned to the users 20 in the crowd that reflect the desirability of the users 20 as recipients of the message. The message delivery function 62 then sends the message to the one or more users 20 selected for message delivery. Preferably, the message is anonymous (i.e., does not identify the sender). The message delivery function 62 may also enable the one or more users 20 that receive the message to respond to the message if desired. Any response is also preferably anonymous.

The persistence layer 44 includes an object mapping layer 63 and a datastore 64. The object mapping layer 63 is preferably implemented in software. The datastore 64 is preferably a relational database, which is implemented in a combination of hardware (i.e., physical data storage hardware) and software (i.e., relational database software). In this embodiment, the business logic layer 42 is implemented in an object-oriented programming language such as, for example, Java. As such, the object mapping layer 63 operates to map objects used in the business logic layer 42 to relational database entities stored in the datastore 64. Note that, in one embodiment, data is stored in the datastore 64 in a Resource Description Framework (RDF) compatible format.

In an alternative embodiment, rather than being a relational database, the datastore 64 may be implemented as an RDF datastore. More specifically, the RDF datastore may be compatible with RDF technology adopted by Semantic Web activities. Namely, the RDF datastore may use the Friend-Of-A-Friend (FOAF) vocabulary for describing people, their social networks, and their interests. In this embodiment, the MAP server 12 may be designed to accept raw FOAF files describing persons, their friends, and their interests. These FOAF files are currently output by some social networking services such as LiveJournal® and Facebook®. The MAP server 12 may then persist RDF descriptions of the users 20 as a proprietary extension of the FOAF vocabulary that includes additional properties desired for the system 10.

FIG. 3 illustrates the MAP client 30 of FIG. 1 in more detail according to one embodiment of the present disclosure. As illustrated, in this embodiment, the MAP client 30 includes a MAP access API 66, a MAP middleware component 68, and a mobile client/server protocol component 70. The MAP access API 66 is implemented in software and provides an interface by which the MAP client 30 and the third-party applications 34 are enabled to access the MAP client 30. The MAP middleware component 68 is implemented in software and performs the operations needed for the MAP client 30 to operate as an interface between the MAP application 32 and the third-party applications 34 at the mobile device 18 and the MAP server 12. The mobile client/server protocol component 70 enables communication between the MAP client 30 and the MAP server 12 via a defined protocol.

FIG. 4 illustrates the operation of the system 10 of FIG. 1 to provide the user profile of one of the users 20 of one of the mobile devices 18 to the MAP server 12 according to one embodiment of the present disclosure. This discussion is equally applicable to the other users 20 of the other mobile devices 18. First, an authentication process is performed (step 1000). For authentication, in this embodiment, the mobile device 18 authenticates with the profile server 14 (step 1000A) and the MAP server 12 (step 1000B). In addition, the MAP server 12 authenticates with the profile server 14 (step 1000C). Preferably, authentication is performed using OpenID or similar technology. However, authentication may alternatively be performed using separate credentials (e.g., username and password) of the user 20 for access to the MAP server 12 and the profile server 14. Assuming that authentication is successful, the profile server 14 returns an authentication succeeded message to the MAP server 12 (step 1000D), and the profile server 14 returns an authentication succeeded message to the MAP client 30 of the mobile device 18 (step 1000E).

At some point after authentication is complete, a user profile process is performed such that a user profile of the user 20 is obtained from the profile server 14 and delivered to the MAP server 12 (step 1002). In this embodiment, the MAP client 30 of the mobile device 18 sends a profile request to the profile server 14 (step 1002A). In response, the profile server 14 returns the user profile of the user 20 to the mobile device 18 (step 1002B). The MAP client 30 of the mobile device 18 then sends the user profile of the user 20 to the MAP server 12 (step 1002C). Note that while in this embodiment the MAP client 30 sends the complete user profile of the user 20 to the MAP server 12, in an alternative embodiment, the MAP client 30 may filter the user profile of the user 20 according to criteria specified by the user 20. For example, the user profile of the user 20 may include demographic information, general interests, music interests, and movie interests, and the user 20 may specify that the demographic information or some subset thereof is to be filtered, or removed, before sending the user profile to the MAP server 12.

Upon receiving the user profile of the user 20 from the MAP client 30 of the mobile device 18, the profile manager 52 of the MAP server 12 processes the user profile (step 1002D). More specifically, in the preferred embodiment, the profile manager 52 includes social network handlers for the social network services supported by the MAP server 12 that operate to map the user profiles of the users 20 obtained from the social network services to a common format utilized by the MAP server 12. This common format includes a number of user profile categories, or user profile slices, such as, for example, a demographic profile category, a social interaction profile category, a general interests category, a music interests profile category, and a movie interests profile category. For example, if the MAP server 12 supports user profiles from Facebook®, MySpace®, and LinkedIN®, the profile manager 52 may include a Facebook handler, a MySpace handler, and a LinkedlN handler. The social network handlers process user profiles from the corresponding social network services to generate user profiles for the users 20 in the common format used by the MAP server 12. For this example assume that the user profile of the user 20 is from Facebook®. The profile manager 52 uses a Facebook handler to process the user profile of the user 20 to map the user profile of the user 20 from Facebook® to a user profile for the user 20 for the MAP server 12 that includes lists of keywords for a number of predefined profile categories, or profile slices, such as, for example, a demographic profile category, a social interaction profile category, a general interests profile category, a music interests profile category, and a movie interests profile category. As such, the user profile of the user 20 from Facebook® may be processed by the Facebook handler of the profile manager 52 to create a list of keywords such as, for example, liberal, High School Graduate, 35-44, College Graduate, etc. for the demographic profile category; a list of keywords such as Seeking Friendship for the social interaction profile category; a list of keywords such as politics, technology, photography, books, etc. for the general interests profile category; a list of keywords including music genres, artist names, album names, or the like for the music interests profile category; and a list of keywords including movie titles, actor or actress names, director names, movie genres, or the like for the movie interests profile category. In one embodiment, the profile manager 52 may use natural language processing or semantic analysis. For example, if the Facebook® user profile of the user 20 states that the user 20 is 20 years old, semantic analysis may result in the keyword of 18-24 years old being stored in the user profile of the user 20 for the MAP server 12.

After processing the user profile of the user 20, the profile manager 52 of the MAP server 12 stores the resulting user profile for the user 20 (step 1002E). More specifically, in one embodiment, the MAP server 12 stores user records for the users 20 in the datastore 64 (FIG. 2). The user profile of the user 20 is stored in the user record of the user 20. The user record of the user 20 includes a unique identifier of the user 20, the user profile of the user 20, and, as discussed below, a current location of the user 20. Note that the user profile of the user 20 may be updated as desired. For example, in one embodiment, the user profile of the user 20 is updated by repeating step 1002 each time the user 20 activates the MAP application 32.

Note that while the discussion herein focuses on an embodiment where the user profiles of the users 20 are obtained from the one or more profile servers 14, the user profiles of the users 20 may be obtained in any desired manner. For example, in one alternative embodiment, the user 20 may identify one or more favorite websites. The profile manager 52 of the MAP server 12 may then crawl the one or more favorite websites of the user 20 to obtain keywords appearing in the one or more favorite websites of the user 20. These keywords may then be stored as the user profile of the user 20.

At some point, a process is performed such that a current location of the mobile device 18 and thus a current location of the user 20 is obtained by the MAP server 12 (step 1004). In this embodiment, the MAP application 32 of the mobile device 18 obtains the current location of the mobile device 18 from the location function 36 of the mobile device 18. The MAP application 32 then provides the current location of the mobile device 18 to the MAP client 30, and the MAP client 30 then provides the current location of the mobile device 18 to the MAP server 12 (step 1004A). Note that step 1004A may be repeated periodically or in response to a change in the current location of the mobile device 18 in order for the MAP application 32 to provide location updates for the user 20 to the MAP server 12.

In response to receiving the current location of the mobile device 18, the location manager 54 of the MAP server 12 stores the current location of the mobile device 18 as the current location of the user 20 (step 1004B). More specifically, in one embodiment, the current location of the user 20 is stored in the user record of the user 20 maintained in the datastore 64 of the MAP server 12. Note that, in the preferred embodiment, only the current location of the user 20 is stored in the user record of the user 20. In this manner, the MAP server 12 maintains privacy for the user 20 since the MAP server 12 does not maintain a historical record of the location of the user 20. Any historical data maintained by the MAP server 12 is preferably anonymized by the history manager 56 in order to maintain the privacy of the users 20.

In addition to storing the current location of the user 20, the location manager 54 sends the current location of the user 20 to the location server 16 (step 1004C). In this embodiment, by providing location updates to the location server 16, the MAP server 12 in return receives location updates for the user 20 from the location server 16. This is particularly beneficial when the mobile device 18 does not permit background processes. If the mobile device 18 does not permit background processes, the MAP application 32 will not be able to provide location updates for the user 20 to the MAP server 12 unless the MAP application 32 is active. Therefore, when the MAP application 32 is not active, other applications running on the mobile device 18 (or some other device of the user 20) may directly or indirectly provide location updates to the location server 16 for the user 20. This is illustrated in step 1006 where the location server 16 receives a location update for the user 20 directly or indirectly from another application running on the mobile device 18 or an application running on another device of the user 20 (step 1006A). The location server 16 then provides the location update for the user 20 to the MAP server 12 (step 1006B). In response, the location manager 54 updates and stores the current location of the user 20 in the user record of the user 20 (step 1006C). In this manner, the MAP server 12 is enabled to obtain location updates for the user 20 even when the MAP application 32 is not active at the mobile device 18.

FIG. 5 illustrates the operation of the system 10 of FIG. 1 to provide the user profile of the user 20 of one of the mobile devices 18 to the MAP server 12 according to another embodiment of the present disclosure. This discussion is equally applicable to user profiles of the users 20 of the other mobile devices 18. First, an authentication process is performed (step 1100). For authentication, in this embodiment, the mobile device 18 authenticates with the MAP server 12 (step 1100A), and the MAP server 12 authenticates with the profile server 14 (step 1100B). Preferably, authentication is performed using OpenID or similar technology. However, authentication may alternatively be performed using separate credentials (e.g., username and password) of the user 20 for access to the MAP server 12 and the profile server 14. Assuming that authentication is successful, the profile server 14 returns an authentication succeeded message to the MAP server 12 (step 1100C), and the MAP server 12 returns an authentication succeeded message to the MAP client 30 of the mobile device 18 (step 1100D).

At some point after authentication is complete, a user profile process is performed such that a user profile of the user 20 is obtained from the profile server 14 and delivered to the MAP server 12 (step 1102). In this embodiment, the profile manager 52 of the MAP server 12 sends a profile request to the profile server 14 (step 1102A). In response, the profile server 14 returns the user profile of the user 20 to the profile manager 52 of the MAP server 12 (step 1102B). Note that while in this embodiment the profile server 14 returns the complete user profile of the user 20 to the MAP server 12, in an alternative embodiment, the profile server 14 may return a filtered version of the user profile of the user 20 to the MAP server 12. The profile server 14 may filter the user profile of the user 20 according to criteria specified by the user 20. For example, the user profile of the user 20 may include demographic information, general interests, music interests, and movie interests, and the user 20 may specify that the demographic information or some subset thereof is to be filtered, or removed, before sending the user profile to the MAP server 12.

Upon receiving the user profile of the user 20, the profile manager 52 of the MAP server 12 processes the user profile (step 1102C). More specifically, as discussed above, in the preferred embodiment, the profile manager 52 includes social network handlers for the social network services supported by the MAP server 12. The social network handlers process user profiles to generate user profiles for the MAP server 12 that include lists of keywords for each of a number of profile categories, or profile slices.

After processing the user profile of the user 20, the profile manager 52 of the MAP server 12 stores the resulting user profile for the user 20 (step 1102D). More specifically, in one embodiment, the MAP server 12 stores user records for the users 20 in the datastore 64 (FIG. 2). The user profile of the user 20 is stored in the user record of the user 20. The user record of the user 20 includes a unique identifier of the user 20, the user profile of the user 20, and, as discussed below, a current location of the user 20. Note that the user profile of the user 20 may be updated as desired. For example, in one embodiment, the user profile of the user 20 is updated by repeating step 1102 each time the user 20 activates the MAP application 32.

Note that while the discussion herein focuses on an embodiment where the user profiles of the users 20 are obtained from the one or more profile servers 14, the user profiles of the users 20 may be obtained in any desired manner. For example, in one alternative embodiment, the user 20 may identify one or more favorite websites. The profile manager 52 of the MAP server 12 may then crawl the one or more favorite websites of the user 20 to obtain keywords appearing in the one or more favorite websites of the user 20. These keywords may then be stored as the user profile of the user 20.

At some point, a process is performed such that a current location of the mobile device 18 and thus a current location of the user 20 is obtained by the MAP server 12 (step 1104). In this embodiment, the MAP application 32 of the mobile device 18 obtains the current location of the mobile device 18 from the location function 36 of the mobile device 18. The MAP application 32 then provides the current location of the user 20 of the mobile device 18 to the location server 16 (step 1104A). Note that step 1104A may be repeated periodically or in response to changes in the location of the mobile device 18 in order to provide location updates for the user 20 to the MAP server 12. The location server 16 then provides the current location of the user 20 to the MAP server 12 (step 1104B). The location server 16 may provide the current location of the user 20 to the MAP server 12 automatically in response to receiving the current location of the user 20 from the mobile device 18 or in response to a request from the MAP server 12.

In response to receiving the current location of the mobile device 18, the location manager 54 of the MAP server 12 stores the current location of the mobile device 18 as the current location of the user 20 (step 1104C). More specifically, in one embodiment, the current location of the user 20 is stored in the user record of the user 20 maintained in the datastore 64 of the MAP server 12. Note that, in the preferred embodiment, only the current location of the user 20 is stored in the user record of the user 20. In this manner, the MAP server 12 maintains privacy for the user 20 since the MAP server 12 does not maintain a historical record of the location of the user 20. As discussed below in detail, historical data maintained by the MAP server 12 is preferably anonymized in order to maintain the privacy of the users 20.

As discussed above, the use of the location server 16 is particularly beneficial when the mobile device 18 does not permit background processes. As such, if the mobile device 18 does not permit background processes, the MAP application 32 will not provide location updates for the user 20 to the location server 16 unless the MAP application 32 is active. However, other applications running on the mobile device 18 (or some other device of the user 20) may provide location updates to the location server 16 for the user 20 when the MAP application 32 is not active. This is illustrated in step 1106 where the location server 16 receives a location update for the user 20 from another application running on the mobile device 18 or an application running on another device of the user 20 (step 1106A). The location server 16 then provides the location update for the user 20 to the MAP server 12 (step 1106B). In response, the location manager 54 updates and stores the current location of the user 20 in the user record of the user 20 (step 1106C). In this manner, the MAP server 12 is enabled to obtain location updates for the user 20 even when the MAP application 32 is not active at the mobile device 18.

FIG. 6 begins a discussion of the operation of the crowd analyzer 58 to form crowds of users according to one embodiment of the present disclosure. Specifically, FIG. 6 is a flow chart for a spatial crowd formation process according to one embodiment of the present disclosure. Note that, in one embodiment, this process is performed in response to a request for crowd data for a POI or an AOI or in response to a crowdsearch request. In another embodiment, this process may be performed proactively by the crowd analyzer 58 as, for example, a background process.

First, the crowd analyzer 58 establishes a bounding box for the crowd formation process (step 1200). Note that while a bounding box is used in this example, other geographic shapes may be used to define a bounding region for the crowd formation process (e.g., a bounding circle). In one embodiment, if crowd formation is performed in response to a specific request, the bounding box is established based on the POI or the AOI of the request. If the request is for a POI, then the bounding box is a geographic area of a predetermined size centered at the POI. If the request is for an AOI, the bounding box is the AOI. Alternatively, if the crowd formation process is performed proactively, the bounding box is a bounding box of a predefined size.

The crowd analyzer 58 then creates a crowd for each individual user in the bounding box (step 1202). More specifically, the crowd analyzer 58 queries the datastore 64 of the MAP server 12 to identify users currently located within the bounding box. Then, a crowd of one user is created for each user currently located within the bounding box. Next, the crowd analyzer 58 determines the two closest crowds in the bounding box (step 1204) and determines a distance between the two crowds (step 1206). The distance between the two crowds is a distance between crowd centers of the two crowds. Note that the crowd center of a crowd of one is the current location of the user in the crowd. The crowd analyzer 58 then determines whether the distance between the two crowds is less than an optimal inclusion distance (step 1208). In this embodiment, the optimal inclusion distance is a predefined static distance. If the distance between the two crowds is less than the optimal inclusion distance, the crowd analyzer 58 combines the two crowds (step 1210) and computes a new crowd center for the resulting crowd (step 1212). The crowd center may be computed based on the current locations of the users in the crowd using a center of mass algorithm. At this point the process returns to step 1204 and is repeated until the distance between the two closest crowds is not less than the optimal inclusion distance. At that point, the crowd analyzer 58 discards any crowds with less than three users (step 1214). Note that throughout this disclosure crowds are only maintained if the crowds include three or more users. However, while three users is the preferred minimum number of users in a crowd, the present disclosure is not limited thereto. The minimum number of users in a crowd may be defined as any number greater than or equal to two users.

FIGS. 7A through 7D graphically illustrate the crowd formation process of FIG. 6 for an exemplary bounding box 72. In FIGS. 7A through 7D, crowds are noted by dashed circles, and the crowd centers are noted by cross-hairs (+). As illustrated in FIG. 7A, initially, the crowd analyzer 58 creates crowds 74 through 82 for the users in the geographic area defined by the bounding box 72, where, at this point, each of the crowds 74 through 82 includes one user. The current locations of the users are the crowd centers of the crowds 74 through 82. Next, the crowd analyzer 58 determines the two closest crowds and a distance between the two closest crowds. In this example, at this point, the two closest crowds are crowds 76 and 78, and the distance between the two closest crowds 76 and 78 is less than the optimal inclusion distance. As such, the two closest crowds 76 and 78 are combined by merging crowd 78 into crowd 76, and a new crowd center (+) is computed for the crowd 76, as illustrated in FIG. 7B. Next, the crowd analyzer 58 again determines the two closest crowds, which are now crowds 74 and 76. The crowd analyzer 58 then determines a distance between the crowds 74 and 76. Since the distance is less than the optimal inclusion distance, the crowd analyzer 58 combines the two crowds 74 and 76 by merging the crowd 74 into the crowd 76, and a new crowd center (+) is computed for the crowd 76, as illustrated in FIG. 7C. At this point, there are no more crowds separated by less than the optimal inclusion distance. As such, the crowd analyzer 58 discards crowds having less than three users, which in this example are crowds 80 and 82. As a result, at the end of the crowd formation process, the crowd 76 has been formed with three users, as illustrated in FIG. 7D.

FIGS. 8A through 8D illustrate a flow chart for a spatial crowd formation process according to another embodiment of the present disclosure. In this embodiment, the spatial crowd formation process is triggered in response to receiving a location update for one of the users 20 and is preferably repeated for each location update received for the users 20. As such, first, the crowd analyzer 58 receives a location update, or a new location, for a user (step 1300). Assume that, for this example, the location update is received for the user 20-1. In response, the crowd analyzer 58 retrieves an old location of the user 20-1, if any (step 1302). The old location is the current location of the user 20-1 prior to receiving the new location. The crowd analyzer 58 then creates a new bounding box of a predetermined size centered at the new location of the user 20-1 (step 1304) and an old bounding box of a predetermined size centered at the old location of the user 20-1, if any (step 1306). The predetermined size of the new and old bounding boxes may be any desired size. As one example, the predetermined size of the new and old bounding boxes is 40 meters by 40 meters. Note that if the user 20-1 does not have an old location (i.e., the location received in step 1300 is the first location received for the user 20-1), then the old bounding box is essentially null. Also note that while bounding “boxes” are used in this example, the bounding areas may be of any desired shape.

Next, the crowd analyzer 58 determines whether the new and old bounding boxes overlap (step 1308). If so, the crowd analyzer 58 creates a bounding box encompassing the new and old bounding boxes (step 1310). For example, if the new and old bounding boxes are 40×40 meter regions and a 1×1 meter square at the northeast corner of the new bounding box overlaps a 1×1 meter square at the southwest corner of the old bounding box, the crowd analyzer 58 may create a 79×79 meter square bounding box encompassing both the new and old bounding boxes.

The crowd analyzer 58 then determines the individual users and crowds relevant to the bounding box created in step 1310 (step 1312). The crowds relevant to the bounding box are crowds that are within or overlap the bounding box (e.g., have at least one user located within the bounding box). The individual users relevant to the bounding box are users that are currently located within the bounding box and not already part of a crowd. Next, the crowd analyzer 58 computes an optimal inclusion distance for individual users based on user density within the bounding box (step 1314). More specifically, in one embodiment, the optimal inclusion distance for individuals, which is also referred to herein as an initial optimal inclusion distance, is set according to the following equation:

$\begin{matrix} {{{{initial\_ optimal}{\_ inclusion}{\_ dist}} = {a \cdot \sqrt{\frac{A_{BoundingBox}}{{number\_ of}{\_ users}}}}},} & {{Eqn}.\mspace{14mu} (1)} \end{matrix}$

where a is a number between 0 and 1, A_(BoundingBox) is an area of the bounding box, and number_of_users is the total number of users in the bounding box. The total number of users in the bounding box includes both individual users that are not already in a crowd and users that are already in a crowd. In one embodiment, a is ⅔.

The crowd analyzer 58 then creates a crowd for each individual user within the bounding box that is not already included in a crowd and sets the optimal inclusion distance for the crowds to the initial optimal inclusion distance (step 1316). At this point, the process proceeds to FIG. 8B where the crowd analyzer 58 analyzes the crowds relevant to the bounding box to determine whether any of the crowd members (i.e., users in the crowds) violate the optimal inclusion distance of their crowds (step 1318). Any crowd member that violates the optimal inclusion distance of his or her crowd is then removed from that crowd (step 1320). The crowd analyzer 58 then creates a crowd of one user for each of the users removed from their crowds in step 1320 and sets the optimal inclusion distance for the newly created crowds to the initial optimal inclusion distance (step 1322).

Next, the crowd analyzer 58 determines the two closest crowds for the bounding box (step 1324) and a distance between the two closest crowds (step 1326). The distance between the two closest crowds is the distance between the crowd centers of the two closest crowds. The crowd analyzer 58 then determines whether the distance between the two closest crowds is less than the optimal inclusion distance of a larger of the two closest crowds (step 1328). If the two closest crowds are of the same size (i.e., have the same number of users), then the optimal inclusion distance of either of the two closest crowds may be used. Alternatively, if the two closest crowds are of the same size, the optimal inclusion distances of both of the two closest crowds may be used such that the crowd analyzer 58 determines whether the distance between the two closest crowds is less than the optimal inclusion distances of both of the two closest crowds. As another alternative, if the two closest crowds are of the same size, the crowd analyzer 58 may compare the distance between the two closest crowds to an average of the optimal inclusion distances of the two closest crowds.

If the distance between the two closest crowds is not less than the optimal inclusion distance, then the process proceeds to step 1338. Otherwise, the two closest crowds are combined or merged (step 1330), and a new crowd center for the resulting crowd is computed (step 1332). Again, a center of mass algorithm may be used to compute the crowd center of a crowd. In addition, a new optimal inclusion distance for the resulting crowd is computed (step 1334). In one embodiment, the new optimal inclusion distance for the resulting crowd is computed as:

$\begin{matrix} {{{average} = {\frac{1}{n + 1} \cdot \left( {{{initial\_ optimal}{\_ inclusion}{\_ dist}} + {\sum\limits_{i = 1}^{n}d_{i}}} \right)}},} & {{Eqn}.\mspace{14mu} (2)} \\ {{{optimal\_ inclusion}{\_ dist}} = {{average} + \sqrt{\left( {\frac{1}{n} \cdot {\sum\limits_{i = 1}^{n}\left( {d_{i} - {average}} \right)^{2}}} \right)}}} & {{Eqn}.\mspace{14mu} (3)} \end{matrix}$

where n is the number of users in the crowd and d_(i) is a distance between the ith user and the crowd center. In other words, the new optimal inclusion distance is computed as the average of the initial optimal inclusion distance and the distances between the users in the crowd and the crowd center plus one standard deviation.

At this point, the crowd analyzer 58 determines whether a maximum number of iterations have been performed (step 1336). The maximum number of iterations is a predefined number that ensures that the crowd formation process does not indefinitely loop over steps 1318 through 1334 or loop over steps 1318 through 1334 more than a desired maximum number of times. If the maximum number of iterations has not been reached, the process returns to step 1318 and is repeated until either the distance between the two closest crowds is not less than the optimal inclusion distance of the larger crowd or the maximum number of iterations has been reached. At that point, the crowd analyzer 58 discards crowds with less than three users, or members (step 1338) and the process ends.

Returning to step 1308 in FIG. 8A, if the new and old bounding boxes do not overlap, the process proceeds to FIG. 8C and the bounding box to be processed is set to the old bounding box (step 1340). In general, the crowd analyzer 58 then processes the old bounding box in much the same manner as described above with respect to steps 1312 through 1338. More specifically, the crowd analyzer 58 determines the individual users and crowds relevant to the bounding box (step 1342). The crowds relevant to the bounding box are crowds that are within or overlap the bounding box (e.g., have at least one user located within the bounding box). The individual users relevant to the bounding box are users that are currently located within the bounding box and not already part of a crowd. Next, the crowd analyzer 58 computes an optimal inclusion distance for individual users based on user density within the bounding box (step 1344). More specifically, in one embodiment, the optimal inclusion distance for individuals, which is also referred to herein as an initial optimal inclusion distance, is set according to the following equation:

$\begin{matrix} {{{{initial\_ optimal}{\_ inclusion}{\_ dist}} = {a \cdot \sqrt{\frac{A_{BoundingBox}}{{number\_ of}{\_ users}}}}},} & {{Eqn}.\mspace{14mu} (4)} \end{matrix}$

where a is a number between 0 and 1, A_(BoundingBox) is an area of the bounding box, and number_of_users is the total number of users in the bounding box. The total number of users in the bounding box includes both individual users that are not already in a crowd and users that are already in a crowd. In one embodiment, a is ⅔.

The crowd analyzer 58 then creates a crowd of one user for each individual user within the bounding box that is not already included in a crowd and sets the optimal inclusion distance for the crowds to the initial optimal inclusion distance (step 1346). At this point, the crowd analyzer 58 analyzes the crowds for the bounding box to determine whether any crowd members (i.e., users in the crowds) violate the optimal inclusion distance of their crowds (step 1348). Any crowd member that violates the optimal inclusion distance of his or her crowd is then removed from that crowd (step 1350). The crowd analyzer 58 then creates a crowd of one user for each of the users removed from their crowds in step 1350 and sets the optimal inclusion distance for the newly created crowds to the initial optimal inclusion distance (step 1352).

Next, the crowd analyzer 58 determines the two closest crowds in the bounding box (step 1354) and a distance between the two closest crowds (step 1356). The distance between the two closest crowds is the distance between the crowd centers of the two closest crowds. The crowd analyzer 58 then determines whether the distance between the two closest crowds is less than the optimal inclusion distance of a larger of the two closest crowds (step 1358). If the two closest crowds are of the same size (i.e., have the same number of users), then the optimal inclusion distance of either of the two closest crowds may be used. Alternatively, if the two closest crowds are of the same size, the optimal inclusion distances of both of the two closest crowds may be used such that the crowd analyzer 58 determines whether the distance between the two closest crowds is less than the optimal inclusion distances of both of the two closest crowds. As another alternative, if the two closest crowds are of the same size, the crowd analyzer 58 may compare the distance between the two closest crowds to an average of the optimal inclusion distances of the two closest crowds.

If the distance between the two closest crowds is not less than the optimal inclusion distance, the process proceeds to step 1368. Otherwise, the two closest crowds are combined or merged (step 1360), and a new crowd center for the resulting crowd is computed (step 1362). Again, a center of mass algorithm may be used to compute the crowd center of a crowd. In addition, a new optimal inclusion distance for the resulting crowd is computed (step 1364). As discussed above, in one embodiment, the new optimal inclusion distance for the resulting crowd is computed as:

$\begin{matrix} {{{average} = {\frac{1}{n + 1} \cdot \left( {{{initial\_ optimal}{\_ inclusion}{\_ dist}} + {\sum\limits_{i = 1}^{n}d_{i}}} \right)}},} & {{Eqn}.\mspace{14mu} (5)} \\ {{{optimal\_ inclusion}{\_ dist}} = {{average} + \sqrt{\left( {\frac{1}{n} \cdot {\sum\limits_{i = 1}^{n}\left( {d_{i} - {average}} \right)^{2}}} \right)}}} & {{Eqn}.\mspace{14mu} (6)} \end{matrix}$

where n is the number of users in the crowd and d_(i) is a distance between the ith user and the crowd center. In other words, the new optimal inclusion distance is computed as the average of the initial optimal inclusion distance and the distances between the users in the crowd and the crowd center plus one standard deviation.

At this point, the crowd analyzer 58 determines whether a maximum number of iterations have been performed (step 1366). If the maximum number of iterations has not been reached, the process returns to step 1348 and is repeated until either the distance between the two closest crowds is not less than the optimal inclusion distance of the larger crowd or the maximum number of iterations has been reached. At that point, the crowd analyzer 58 discards crowds with less than three users, or members (step 1368). The crowd analyzer 58 then determines whether the crowd formation process for the new and old bounding boxes is done (step 1370). In other words, the crowd analyzer 58 determines whether both the new and old bounding boxes have been processed. If not, the bounding box is set to the new bounding box (step 1372), and the process returns to step 1342 and is repeated for the new bounding box. Once both the new and old bounding boxes have been processed, the crowd formation process ends.

FIGS. 9A through 9D graphically illustrate the crowd formation process of FIGS. 8A through 8D for a scenario where the crowd formation process is triggered by a location update for a user having no old location. In this scenario, the crowd analyzer 58 creates a new bounding box 84 for the new location of the user, and the new bounding box 84 is set as the bounding box to be processed for crowd formation. Then, as illustrated in FIG. 9A, the crowd analyzer 58 identifies all individual users currently located within the new bounding box 84 and all crowds located within or overlapping the new bounding box 84. In this example, crowd 86 is an existing crowd relevant to the new bounding box 84. Crowds are indicated by dashed circles, crowd centers are indicated by cross-hairs (+), and users are indicated as dots. Next, as illustrated in FIG. 9B, the crowd analyzer 58 creates crowds 88 through 92 of one user for the individual users, and the optional inclusion distances of the crowds 88 through 92 are set to the initial optimal inclusion distance. As discussed above, the initial optimal inclusion distance is computed by the crowd analyzer 58 based on a density of users within the new bounding box 84.

The crowd analyzer 58 then identifies the two closest crowds 88 and 90 in the new bounding box 84 and determines a distance between the two closest crowds 88 and 90. In this example, the distance between the two closest crowds 88 and 90 is less than the optimal inclusion distance. As such, the two closest crowds 88 and 90 are merged and a new crowd center and new optimal inclusion distance are computed, as illustrated in FIG. 9C. The crowd analyzer 58 then repeats the process such that the two closest crowds 88 and 92 in the new bounding box 84 are again merged, as illustrated in FIG. 9D. At this point, the distance between the two closest crowds 86 and 88 is greater than the appropriate optimal inclusion distance. As such, the crowd formation process is complete.

FIGS. 10A through 10F graphically illustrate the crowd formation process of FIGS. 8A through 8D for a scenario where the new and old bounding boxes overlap. As illustrated in FIG. 10A, a user moves from an old location to a new location, as indicated by an arrow. The crowd analyzer 58 receives a location update for the user giving the new location of the user. In response, the crowd analyzer 58 creates an old bounding box 94 for the old location of the user and a new bounding box 96 for the new location of the user. Crowd 98 exists in the old bounding box 94, and crowd 100 exists in the new bounding box 96.

Since the old bounding box 94 and the new bounding box 96 overlap, the crowd analyzer 58 creates a bounding box 102 that encompasses both the old bounding box 94 and the new bounding box 96, as illustrated in FIG. 10B. In addition, the crowd analyzer 58 creates crowds 104 through 110 for individual users currently located within the bounding box 102. The optimal inclusion distances of the crowds 104 through 110 are set to the initial optimal inclusion distance computed by the crowd analyzer 58 based on the density of users in the bounding box 102.

Next, the crowd analyzer 58 analyzes the crowds 98, 100, and 104 through 110 to determine whether any members of the crowds 98, 100, and 104 through 110 violate the optimal inclusion distances of the crowds 98, 100, and 104 through 110. In this example, as a result of the user leaving the crowd 98 and moving to his new location, both of the remaining members of the crowd 98 violate the optimal inclusion distance of the crowd 98. As such, the crowd analyzer 58 removes the remaining users from the crowd 98 and creates crowds 112 and 114 of one user each for those users, as illustrated in FIG. 10C.

The crowd analyzer 58 then identifies the two closest crowds in the bounding box 102, which in this example are the crowds 108 and 110. Next, the crowd analyzer 58 computes a distance between the two crowds 108 and 110. In this example, the distance between the two crowds 108 and 110 is less than the initial optimal inclusion distance and, as such, the two crowds 108 and 110 are combined. In this example, crowds are combined by merging the smaller crowd into the larger crowd. Since the two crowds 108 and 110 are of the same size, the crowd analyzer 58 merges the crowd 110 into the crowd 108, as illustrated in FIG. 10D. A new crowd center and new optimal inclusion distance are then computed for the crowd 108.

At this point, the crowd analyzer 58 repeats the process and determines that the crowds 100 and 106 are now the two closest crowds. In this example, the distance between the two crowds 100 and 106 is less than the optimal inclusion distance of the larger of the two crowds 100 and 106, which is the crowd 100. As such, the crowd 106 is merged into the crowd 100 and a new crowd center and optimal inclusion distance are computed for the crowd 100, as illustrated in FIG. 10E. At this point, there are no two crowds closer than the optimal inclusion distance of the larger of the two crowds. As such, the crowd analyzer 58 discards any crowds having less than three members, as illustrated in FIG. 10F. In this example, the crowds 104, 108, 112, and 114 have less than three members and are therefore removed. The crowd 100 has three or more members and, as such, is not removed. At this point, the crowd formation process is complete.

FIGS. 11A through 11E graphically illustrate the crowd formation process of FIGS. 8A through 8D in a scenario where the new and old bounding boxes do not overlap. As illustrated in FIG. 11A, in this example, the user moves from an old location to a new location. The crowd analyzer 58 creates an old bounding box 116 for the old location of the user and a new bounding box 118 for the new location of the user. Crowds 120 and 122 exist in the old bounding box 116, and crowd 124 exists in the new bounding box 118. In this example, since the old and new bounding boxes 116 and 118 do not overlap, the crowd analyzer 58 processes the old and new bounding boxes 116 and 118 separately.

More specifically, as illustrated in FIG. 11B, as a result of the movement of the user from the old location to the new location, the remaining users in the crowd 120 no longer satisfy the optimal inclusion distance for the crowd 120. As such, the remaining users in the crowd 120 are removed from the crowd 120, and crowds 126 and 128 of one user each are created for the removed users as shown in FIG. 11C. In this example, no two crowds in the old bounding box 116 are close enough to be combined. As such, crowds having less than three users are removed, and processing of the old bounding box 116 is complete, and the crowd analyzer 58 proceeds to process the new bounding box 118.

As illustrated in FIG. 11D, processing of the new bounding box 118 begins by the crowd analyzer 58 creating a crowd 130 of one user for the user. The crowd analyzer 58 then identifies the crowds 124 and 130 as the two closest crowds in the new bounding box 118 and determines a distance between the two crowds 124 and 130. In this example, the distance between the two crowds 124 and 130 is less than the optimal inclusion distance of the larger crowd, which is the crowd 124. As such, the crowd analyzer 58 combines the crowds 124 and 130 by merging the crowd 130 into the crowd 124, as illustrated in FIG. 11E. A new crowd center and new optimal inclusion distance are then computed for the crowd 124. At this point, the crowd formation process is complete. Note that the crowd formation processes described above with respect to FIGS. 6 through 11D are exemplary. The present disclosure is not limited thereto. Any type of crowd formation process may be used.

FIG. 12 illustrates the operation of the system 10 to enable generation and delivery of a message to a select subset of users in a select crowd according to one embodiment of the present disclosure. First, the mobile device 18-1 sends a crowd request to the MAP server 12 (step 1400). Note that while in this example the crowd request and, as discussed below, message originate from the mobile device 18-1 of the user 20-1, this discussion is equally applicable to crowd requests and messages originating from the mobile devices 18 of any of the users 20. The crowd request is a request for crowd data for crowds currently formed near a specified POI or within a specified AOI. The crowd request may be initiated by the user 20-1 of the mobile device 18-1 via the MAP application 32-1 or may be initiated automatically by the MAP application 32-1 in response to an event such as, for example, start-up of the MAP application 32-1, movement of the user 20-1, or the like.

In one embodiment, the crowd request is for a POI, where the POI is a POI corresponding to the current location of the user 20-1, a POI selected from a list of POIs defined by the user 20-1, a POI selected from a list of POIs defined by the MAP application 32-1 or the MAP server 12, a POI selected by the user 20-1 from a map, a POI implicitly defined via a separate application (e.g., the POI is implicitly defined as the location of the nearest Starbucks coffee house in response to the user 20-1 performing a Google search for “Starbucks”), or the like. If the POI is selected from a list of POIs, the list of POIs may include static POIs which may be defined by street addresses or latitude and longitude coordinates, dynamic POIs which may be defined as the current locations of one or more friends of the user 20-1, or both. Note that in some embodiments, the user 20-1 may be enabled to define a POI by selecting a crowd center of a crowd as a POI, where the POI would thereafter remain static at that point and would not follow the crowd.

In another embodiment, the crowd request is for an AOI, where the AOI may be an AOI of a predefined shape and size centered at the current location of the user 20-1, an AOI selected from a list of AOIs defined by the user 20-1, an AOI selected from a list of AOIs defined by the MAP application 32-1 or the MAP server 12, an AOI selected by the user 20-1 from a map, an AOI implicitly defined via a separate application (e.g., the AOI is implicitly defined as an area of a predefined shape and size centered at the location of the nearest Starbucks coffee house in response to the user 20-1 performing a Google search for “Starbucks”), or the like. If the AOI is selected from a list of AOIs, the list of AOIs may include static AOIs, dynamic AOIs which may be defined as areas of a predefined shape and size centered at the current locations of one or more friends of the user 20-1, or both. Note that in some embodiments, the user 20-1 may be enabled to define an AOI by selecting a crowd such that an AOI is created of a predefined shape and size centered at the crowd center of the selected crowd. The AOI would thereafter remain static and would not follow the crowd. The POI or the AOI of the crowd request may be selected by the user 20-1 via the MAP application 32-1. In yet another embodiment, the MAP application 32-1 automatically uses the current location of the user 20-1 as the POI or as a center point for an AOI of a predefined shape and size.

Upon receiving the crowd request, the MAP server 12 identifies one or more crowds relevant to the crowd request (step 1402). More specifically, in one embodiment, the crowd analyzer 58 performs a crowd formation process such as that described above in FIG. 6 to form one or more crowds relevant to the POI or the AOI of the crowd request. In another embodiment, the crowd analyzer 58 proactively forms crowds using a process such as that described above in FIGS. 8A through 8D and stores corresponding crowd records in the datastore 64 of the MAP server 12. Then, rather than forming the relevant crowds in response to the crowd request, the crowd analyzer 58 queries the datastore 64 to identify the crowds that are relevant to the crowd request. The crowds relevant to the crowd request may be those crowds within or intersecting a bounding region, such as a bounding box, for the crowd request (e.g., crowds having crowd centers within the bounding region for the crowd request, crowds having one or more users located within the bounding region for the crowd request, or crowds having crowd perimeters that are within or overlap the bounding region for the crowd request). If the crowd request is for a POI, the bounding region is a geographic region of a predefined shape and size centered at the POI. If the crowd request is for an AOI, the bounding region is the AOI.

Once the crowd analyzer 58 has identified the crowds relevant to the crowd request, the MAP server 12 generates or otherwise obtains crowd data for the identified crowds (step 1404). The crowd data for the identified crowds preferably includes spatial information defining the locations of the crowds (e.g., the crowd centers of the crowds or North-West and South-East corners of a bounding box passing through the outermost users in the crowd), aggregate profiles for the crowds, information characterizing the crowds, or both. In addition, the crowd data may include the number of users in the crowds, the amount of time the crowds have been located at or near the POI or within the AOI of the crowd request, or the like. The MAP server 12 then returns the crowd data to the mobile device 18-1 (step 1406).

Upon receiving the crowd data, the MAP application 32-1 of the mobile device 18-1 presents the crowd data to the user 20-1 (step 1408). The manner in which the crowd data is presented depends on the particular implementation of the MAP application 32-1. In one embodiment, the crowd data is overlaid upon a map. For example, the crowds may be represented by corresponding indicators overlaid on a map. The user 20-1 may then select a crowd in order to view additional crowd data regarding that crowd such as, for example, the aggregate profile of that crowd, characteristics of that crowd, or the like.

Next, the MAP application 32-1 of the mobile device 18-1 receives user input from the user 20-1 that selects a crowd (also referred to herein as a desired crowd) to which to send a message (step 1410). In response, the MAP application 32-1 of the mobile device 18-1 generates a message to be sent to a subset of users in the crowd (step 1412). More specifically, in the preferred embodiment, the message includes information that identifies the desired crowd selected in step 1410, a message body, and one or more message delivery criteria. Thus, the message is essentially addressed using the information that identifies the desired crowd and the one or more message delivery criteria. The information that identifies the desired crowd may be, for example, a unique identifier assigned to the crowd by the MAP server 12. The message body is preferably a text message input by the user 20-1 of the mobile device 18-1 but is not limited thereto. For example, the message body may alternatively be an audio message (e.g., a voice message) or an audio-visual message (e.g., a video message).

The message delivery criteria preferably include one or more user scoring criteria and one or more selection criteria. The one or more user scoring criteria preferably include one or more of the following:

-   -   one or more select profile categories from the user profile of         the user 20-1 to be matched against user profiles of the users         in the desired crowd (or corresponding profile categories in the         user profiles of the users in the desired crowd) in order to         select the subset of the users in the crowd to which to deliver         the message,     -   weights assigned to the one or more select profile categories,     -   an implicit referral rating criterion such that the subset of         users in the desired crowd to which to deliver the message are         selected based on implicit referral ratings of the users in the         desired crowd,     -   a weight assigned to the implicit referral rating criterion,     -   an explicit referral rating criterion such that the subset of         users in the desired crowd to which to deliver the message are         selected based on explicit referral ratings of the users in the         desired crowd,     -   a weight assigned to the explicit referral rating criterion,     -   a responsiveness rating criterion such that the subset of users         in the desired crowd to which to deliver the message are         selected based on responsiveness ratings of the users in the         desired crowd, and     -   a weight assigned to the responsiveness rating criterion.

The one or more selection criteria preferably include one or more criteria for selecting the subset of users to which to deliver the message based on scores assigned to each of at least some, and possibly all, of the users in the desired crowd based on the one or more user scoring criteria. For example, the one or more selection criteria may include a criterion that a defined number (e.g., 5) of the highest scored users in the crowd are to be selected as the subset of users in the crowd to which the message is to be delivered. As another example, the one or more selection criteria may include a criterion that the users in the crowd that are scored above a defined minimum threshold score (e.g., 50) are to be selected as the subset of users in the crowd to which the message is to be delivered. The message delivery criteria are preferably selected by the user 20-1. Alternatively, the user 20-1 may choose to use predefined default message delivery criteria.

Once the message is generated, the MAP application 32-1 of the mobile device 18-1 sends the message to the MAP server 12 (step 1414). In response to receiving the message, the message delivery function 62 of the MAP server 12 selects the subset of users in the desired crowd to which to send the message (step 1416). As discussed below in detail, in the preferred embodiment, the message delivery function 62 scores at least some of the users in the desired crowd, and possibly all of the users in the desired crowd, based on the one or more user selection criteria included in the message delivery criteria. Then, the message delivery function 62 selects the subset of the users in the desired crowd to which the message is to be delivered based on the scores assigned to the users in the desired crowd and the one or more selection criteria included in the message delivery criteria. It should be noted that while in the embodiments described herein all of the message delivery criteria are included in the message, the present disclosure is not limited thereto. For instance, some or all of the message delivery criteria may be predefined criteria selected by the user 20-1 and stored at the MAP server 12. As another example, some of the message delivery criteria described herein may be system-defined criteria rather than user-defined criteria. For instance, the selection criteria may be system-defined criteria that is stored at the MAP server 12 or embedded into the operation of the message delivery function 62.

In this example, the users 20-2 and 20-3 are selected as the subset of the users in the desired crowd to which the message is to be delivered. As such, the message delivery function 62 of the MAP server 12 sends the message to the mobile devices 18-2 and 18-3 of the users 20-2 and 20-3 (steps 1418 and 1420). Note that the message sent in steps 1418 and 1420 may be a version of the message received by the MAP server 12 in step 1414 that includes the message body but that does not include the message delivery criteria. Further, in the preferred embodiment, the message sent in steps 1418 and 1420 is sent anonymously such that the message does not identify the user 20-1 (i.e., the sender of the message is anonymous). Also, note that an advertisement may be inserted into the message sent to the users 20-2 and 20-3 using any desired targeted advertisement scheme.

The mobile devices 18-2 and 18-3, and preferably the MAP applications 32-2 and 32-3 of the mobile devices 18-2 and 18-3, present the message to the users 20-2 and 20-3 (steps 1422 and 1424). In this example, the MAP application 32-2 of the mobile device 18-2 generates a response to the message based on user input from the user 20-2 (step 1426). The response preferably includes a text, audio, or audio-visual message input by the user 20-2 in response to the message received in step 1422. The MAP application 32-2 then sends the response to the MAP server 12 (step 1428), and the message delivery function 62 of the MAP server 12 then sends the response to the mobile device 18-1 of the user 20-1 (step 1430). Preferably, the response sent to the mobile device 18-1 is sent anonymously such that the response does not identify the user 20-2 (i.e., the responder is anonymous). Also, note that an advertisement may be inserted into the response sent to the user 20-1 using any desired targeted advertisement scheme.

Next, the MAP application 32-1 of the mobile device 18-1 presents the response to the user 20-1 (step 1432). Optionally, in this embodiment, the MAP application 32-1 of the mobile device 18-1 receives a rating assigned to the response by the user 20-1 (step 1434). The rating may be, for example, a numerical rating (e.g., 1 to 5), a like/dislike rating, or the like. The MAP application 32-1 then sends the rating to the MAP server 12 (step 1436). The message delivery function 62 of the MAP server 12 then updates an explicit referral rating of the user 20-2 that sent the response based on the rating assigned to the response by the user 20-1 and received by the MAP server 12 in step 1436 (step 1438). For example, the explicit referral rating of the user 20-2 may be an average rating assigned to responses sent by the user 20-2 in response to messages sent to the user 20-2 by the message delivery function 62 of the MAP server 12 scaled to a value between 0 and 100. In a similar manner, explicit referral ratings may also be maintained for the other users 20 in the system 10.

In this embodiment, the message delivery function 62 of the MAP server 12 also maintains implicit referral ratings for the users 20 in the system 10 based on the ability of the users 20 to submit responses that draw other users 20 to their crowds or, alternatively, POI. More specifically, in this example, the message delivery function 62 monitors the location of the user 20-1 (i.e., the sender) to detect whether the user 20-1 joins the crowd of the user 20-2 (i.e., the responder) within a predefined amount of time after the response is sent in step 1430 (step 1440). Alternatively, the message delivery function 62 may detect whether the user 20-1 comes to the POI at which the desired crowd was located at the time of sending the message within the predefined amount of time. If the user 20-1 joins the crowd of the user 20-2 within the predefined amount of time (e.g., 1 hour), the message delivery function 62 determines that the response of the user 20-2 enticed the user 20-1 to come and join the desired crowd of the user 20-2.

After the user 20-1 has joined the crowd of the user 20-2 or after the predefined amount of time has expired without the user 20-1 joining the crowd of the user 20-2, the message delivery function 62 updates the implicit referral rating of the user 20-2 (step 1442). In one embodiment, the message delivery function 62 includes a counter for the user 20-2 that is incremented if the user 20-1 joins the crowd of the user 20-2 within the predefined amount of time. This counter defines the number of times that the user 20-2 has provided responses that resulted in the other user coming to join the crowd of the user 20-2. The implicit referral rating of the user 20-2 may then be computed as:

$\begin{matrix} {{{referral\_ rating}_{implicit} = {\frac{counter}{{Number\_ of}{\_ Responses}} \times 100}},} & {{Eqn},\mspace{14mu} (7)} \end{matrix}$

where referral_rating_(implicit) is the implicit referral rating of the user 20-2, counter is the counter maintained for the number of times the user 20-2 has provided responses that resulted in the other user coming to join the crowd of the user 20-2, and Number_of_Responses is the total number of responses sent by the user 20-2 to messages sent to the user 20-2 by the message delivery function 62 of the MAP server 12. In a similar manner, implicit referral ratings are preferably maintained for the other users 20.

In addition to the explicit and implicit referral ratings, the message delivery function 62 may also maintain responsiveness ratings for the users 20. In this example, the message delivery function 62 updates the responsiveness ratings of the users 20-2 and 20-3 to which the message was sent in steps 1418 and 1420 (step 1444). As an example, the responsiveness rating of each of the users 20 may be computed based on the following equation:

$\begin{matrix} {{{responsiveness\_ rating} = {\frac{{Number\_ of}{\_ Responses}}{{Number\_ of}{\_ Messages}{\_ Recieved}} \times 100}},} & {{Eqn}.\mspace{14mu} (8)} \end{matrix}$

where responsiveness_rating is the responsiveness rating of the user 20, Number_of_Responses is the number of responses sent by the user 20 in response to messages sent to the user 20 by the message delivery function 62 of the MAP server 12, and Number_of_Messages Received is the number of messages sent to the user 20 by the message delivery function 62.

Before proceeding, again, note that advertisements may be inserted into the message sent to the users 20-2 and 20-3 in the crowd and/or the response sent to the user 20-1 using any desired targeted advertisement scheme. For example, the response to the user 20-1 may include an advertisement for a POI at which the crowd is located. In such a scenario, an ad value may be determined for the advertisement based on the implicit and/or explicit referral rating of the responder and/or a rating for the user 20-1 that is indicative of the likelihood that the user 20-1 will go to and join the crowd of the responder after receiving the response. This later rating may be maintained by the message delivery function 62 by tracking whether the user 20-1 goes to and joins crowds after sending messages to subsets of users in those crowds and receiving responses from users in those crowds.

FIG. 13 illustrates step 1416 of FIG. 12 in more detail according to one embodiment of the present disclosure. In order to select the subset of the users in the desired crowd to which to send the message, the message delivery function 62 first identifies users in the desired crowd that have enabled a messaging feature that allows messages to be sent to them via the message delivery function 62 of the MAP server 12 (step 1500). Note that step 1500 is optional. Alternatively, messages may be sent to all of the users 20 as part of the normal operation of the system 10 where the users 20 cannot disable the messaging feature provided by the message delivery function 62.

Next, a counter i is set to 1 (step 1502). The message delivery function 62 then scores user i of the users identified in step 1500 (or alternatively user i of the users in the desired crowd) based on one or more user scoring criteria included in the message delivery criteria (step 1504). More specifically, in this embodiment, the user scoring criteria include one or more select profile categories from the user profile of the sending user, which in this example is the user 20-1; weights assigned to the one or more select profile categories; an implicit referral rating criterion; a weight assigned to the implicit referral rating criterion; an explicit referral rating criterion; a weight assigned to the explicit referral rating criterion; a responsiveness rating criterion; and a weight assigned to the responsiveness rating criterion. As such, user i is scored by first comparing the one or more select profile categories from the user profile of the user 20-1 to the user profile of user i to determine, for each select profile category, a match value indicative of a degree of similarity between the keywords, or interests, in the select profile category of the user 20-1 and keywords, or interests, in the user profile of user i. The profile matching process may consider all keywords in the user profile of user i or only those keywords in the profile categories of the user profile of user i that correspond to the one or more select profile categories of the user 20-1. In the preferred embodiment, for each of the select profile categories, the match value is the ratio of the number of keywords in the select profile category that have matching keywords in the user profile of user i over the total number of keywords in the select profile category scaled to a value between 0 and 100. Note that scaling to a value between 0 and 100 is exemplary and not intended to limit the scope of the present disclosure. As used herein, two keywords match if the two keywords match to at least a predefined threshold degree. The predefined threshold degree may be an exact match or something less than an exact match (e.g., NC State University determined to match NCSU). For example, for each profile category, the match value may be computed as:

$\begin{matrix} {{{match\_ value} = {\frac{{Number\_ of}{\_ Matches}}{{Total\_ Number}{\_ of}{\_ Keywords}} \times 100}},} & {{Eqn}.\mspace{14mu} (9)} \end{matrix}$

where match_value is the match value for the profile category, Number_of_Matches is the number of keywords in the profile category for which a matching keyword was found in the user profile of user i, and Total_Number_of_Keywords is the total number of keywords in the select profile category of the user profile of the user 20-1.

Once profile matching is complete, user i is scored based on the following equation:

$\begin{matrix} {{{score}_{i} = \frac{\sum\limits_{j = 1}^{M}\left( {w_{j} \times {paramter}_{j}} \right)}{\sum\limits_{j = 1}^{M}w_{j}}},} & {{Eqn}.\mspace{14mu} (10)} \end{matrix}$

where score, is the score for user i, parameter, is a j-th parameter used for the scoring process, w_(j) is a weight assigned to the j-th parameter, and M is the total number of parameters used for the scoring process. In this example, the parameters are the match values computed for the select profile categories of the user profile of the user 20-1, the implicit referral rating of user i, the explicit referral rating of user i, and the responsiveness rating of user i.

Next, the message delivery function 62 determines whether user i is the last user in the users identified in step 1500 (or alternatively all users in the desired crowd) (step 1506). If not, the counter i is incremented (step 1508) and the process is repeated for the next user. Once all of the users have been scored, the message delivery function 62 selects the subset of users to which to send the message based on the one or more selection criteria included in the message delivery criteria and the scores assigned to the users in step 1504 (step 1510). As one example, the one or more selection criteria define a maximum number of users to which to send the message, where the maximum number of users to which to send the message is preferably less than the total number of users in the desired crowd. The message delivery function 62 then selects the maximum number of users in the desired crowd having the highest scores. For example, if the maximum number of users is 5, then the message delivery function 62 selects the 5 highest scored users as the subset of the users in the desired crowd to which to send the message. As another example, the one or more selection criteria may define a minimum threshold score, and the message delivery function 62 selects users having scores that are greater than the minimum threshold score as the subset of users in the desired crowd to which the message is to be sent. The minimum threshold score defined by the one or more selection criteria is preferably greater than a lowest possible score, which in the exemplary embodiment described above is a score of 0.

FIGS. 14A through 14F graphically illustrate the process of FIG. 12 for an exemplary message and response according to one embodiment of the present disclosure. As illustrated in FIG. 14A, after sending the crowd request and receiving corresponding crowd data from the MAP server 12, the MAP application 32-1 of the mobile device 18-1 of the user 20-1 presents the crowd data to the user 20-1 in the form of a number of crowd indicators 132 through 140 overlaid on a map. The crowd indicators 132 through 140 show the locations of corresponding crowds.

As also illustrated in FIG. 14A, the user 20-1 is enabled to select a desired crowd by selecting the crowd indicator 138 of the desired crowd. In response, a menu 142 is presented to the user 20-1. The menu 142 includes a crowd data display area 144 in which information regarding the crowd is presented to the user 20-1 and a menu selection area 146. In this example, the information regarding the crowd includes information describing a POI at which the crowd is currently located and aggregate profile data for the crowd. In this example, the information describing the POI is the name of the POI, the address of the POI, and the telephone number for the POI. Further, in this example, the aggregate profile data for the crowd includes the size of the crowd (i.e., the total number of users in the crowd), a degree of similarity between the crowd and the user profile of the user 20-1 (or one or more select profile categories of the user profile of the user 20-1), and a number of users in the crowd that are addressable (i.e., have enabled the messaging feature provided by the message delivery function 62 of the MAP server 12). Note that the degree of similarity may be calculated as the ratio of the total number of user matches (i.e., the total number of users in the crowd that have at least one keyword that matches a keyword in the user profile of the user 20-1 or one or more select profile categories of the user profile of the user 20-1) over the total number of users in the crowd multiplied by 100.

The menu selection area 146 provides a list of actions that may be selected by the user 20-1. Specifically, in this example, the menu selection area 146 lists a “Take Me There” action, a “Send a MSG to All Members” action, a “Send a MSG to Some Members” action, and a “More . . . ” action. The “Take Me There” action, if selected by the user 20-1, causes the MAP application 32-1 to route the user 20-1 to the POI at which the crowd is located. The “Send a MSG to All Members” action, if selected, enables the user 20-1 to send a message (preferably anonymously) to all of the users in the crowd. A message sent to all of the users in the crowd would be delivered by the message delivery function 62 in much the same manner as described above. However, the message delivery function 62 would not select a subset of users in the crowd to which to deliver the message. Rather, the message delivery function 62 would send the message to all of the users in the crowd, or at least all users in the crowd that have enabled the messaging feature of the message delivery function 62. The “Send a MSG to Some Members” action, if selected by the user 20-1, causes the MAP application 32-1 to generate a message to be delivered to a subset of the users in the crowd in the manner described above. The “More . . . ” action, if selected by the user 20-1, causes the MAP application 32-1 to present more actions to the user 20-1.

In this example, the user 20-1 selects the “Send a MSG to Some Members” action. In response, a message creation screen 148 is presented to the user 20-1 as illustrated in FIG. 14B. The message creation screen 148 includes a text entry area 150 that enables the user 20-1 to enter text for the body of the message. The message creation screen 148 also includes a “Send” button 152 and an “Advanced” button 154. The user 20-1 causes the message to be sent by selecting the “Send” button 152. If the user 20-1 does not first select the “Advanced” button 154 to define message delivery criteria for the message before selecting the “Send” button 152, default message delivery criteria may be used.

In this example, the user 20-1 selects the “Advanced” button 154. In response, a message delivery criteria screen 156 is presented to the user 20-1 as illustrated in FIG. 14C. The message delivery criteria screen 156 includes a “Profile Matching” checkbox 158 and checkboxes 160 through 166 for profile categories in the user profile of the user 20-1. The user 20-1 selects the “Profile Matching” checkbox 158 to cause profile matching to be used when selecting the subset of users in the desired crowd to which the message is to be delivered. Once profile matching is selected, the user 20-1 selects one or more of the checkboxes 160 through 166 to select the corresponding profile categories for use in the profile matching process when selecting the subset of users in the desired crowd to which to deliver the message. In this example, the user 20-1 has selected the “Demographics” profile category and the “Music” profile category. In addition, the message delivery criteria screen 156 includes slider bars 168 through 174 for assigning weights to the corresponding profile categories.

The message delivery criteria screen 156 also includes a “Ratings” checkbox 176 that, when selected by the user 20-1, causes ratings to be used when selecting the subset of users in the desired crowd to which the message is to be delivered. Once the “Ratings” checkbox 176 is selected, in this embodiment, the user 20-1 is enabled to select one or more rating types to use when selecting the subset of the users in the crowd by selecting corresponding checkboxes 178 and 180. In this example, the user 20-1 has enabled the use of both implicit and explicit referral ratings by selecting the checkbox 178 and enabled the use of responsiveness ratings by selecting the checkbox 180. In this example, the message delivery criteria screen 156 also includes slider bars 182 and 184 for assigning weights to the referral ratings and responsiveness ratings, respectively.

Lastly, the message delivery criteria screen 156 includes a pull-down menu 186 that enables the user 20-1 to select a selection criterion to be used to select the subset of users in the desired crowd to which to deliver the message based on the scores assigned to the users in the crowd based on the user scoring criteria. In this example, the user 20-1 has set the selection criterion such that the message is to be delivered to the 5 highest matching (i.e., the 5 highest scored) users in the desired crowd. Once the user 20-1 has finished configuring the message delivery criteria, the user 20-1 selects a “Done” button 188. The user 20-1 can reset the message delivery criteria to their default settings by selecting a “Default” button 190.

Once the message has been sent to the mobile device 18-2 of the user 20-2, the mobile device 18-2 presents the message to the user 20-2 via a message display screen 192, as illustrated in FIG. 14D. The user 20-2 may then read the message and either ignore the message by selecting an “Ignore” button 194 or respond to the message by selecting a “Respond” button 196. In this example, the user 20-2 selects the “Respond” button 196. In response, a response generation screen 198 is presented to the user 20-2, as illustrated in FIG. 14E. The response generation screen 198 includes a text entry area 200 where the user 20-2 is enabled to enter text for a response to be sent to the user 20-1. Once the user 20-2 is ready to send the response, the user 20-2 selects a “Send” button 202. In response, the message is sent to the user 20-1, preferably anonymously, via the message delivery function 62. The user 20-2 may cancel the response generation process by selecting a “Cancel” button 203. Once the response is received by the mobile device 18-1 of the user 20-1, the mobile device 18-1 presents the response to the user 20-1 via a response display screen 204, as illustrated in FIG. 14F. The user 20-1 may then rate the response by selecting either a “Dislike” button 206 or a “Like” button 208.

FIG. 15 is a block diagram of the MAP server 12 according to one embodiment of the present disclosure. As illustrated, the MAP server 12 includes a controller 210 connected to memory 212, one or more secondary storage devices 214, and a communication interface 216 by a bus 218 or similar mechanism. The controller 210 is a microprocessor, digital Application Specific Integrated Circuit (ASIC), Field Programmable Gate Array (FPGA), or the like. In this embodiment, the controller 210 is a microprocessor, and the application layer 40, the business logic layer 42, and the object mapping layer 63 (FIG. 2) are implemented in software and stored in the memory 212 for execution by the controller 210. Further, the datastore 64 (FIG. 2) may be implemented in the one or more secondary storage devices 214. The secondary storage devices 214 are digital data storage devices such as, for example, one or more hard disk drives. The communication interface 216 is a wired or wireless communication interface that communicatively couples the MAP server 12 to the network 28 (FIG. 1). For example, the communication interface 216 may be an Ethernet interface, local wireless interface such as a wireless interface operating according to one of the suite of IEEE 802.11 standards, or the like.

FIG. 16 is a block diagram of the mobile device 18-1 according to one embodiment of the present disclosure. This discussion is equally applicable to the other mobile devices 18-2 through 18-N. As illustrated, the mobile device 18-1 includes a controller 220 connected to memory 222, a communication interface 224, one or more user interface components 226, and the location function 36-1 by a bus 228 or similar mechanism. The controller 220 is a microprocessor, digital ASIC, FPGA, or the like. In this embodiment, the controller 220 is a microprocessor, and the MAP client 30-1, the MAP application 32-1, and the third-party applications 34-1 are implemented in software and stored in the memory 222 for execution by the controller 220. In this embodiment, the location function 36-1 is a hardware component such as, for example, a GPS receiver. The communication interface 224 is a wireless communication interface that communicatively couples the mobile device 18-1 to the network 28 (FIG. 1). For example, the communication interface 224 may be a local wireless interface such as a wireless interface operating according to one of the suite of IEEE 802.11 standards, a mobile communications interface such as a cellular telecommunications interface (e.g., 3G cellular interface such as, for example, a Global System for Mobile communications (GSM) interface or a W-CDMA interface, or a 4G cellular interface such as a Long Term Evolution (LTE) or WiMAX® interface), or the like. The one or more user interface components 226 include, for example, a touchscreen, a display, one or more user input components (e.g., a keypad), a speaker, or the like, or any combination thereof.

Those skilled in the art will recognize improvements and modifications to the preferred embodiments of the present disclosure. All such improvements and modifications are considered within the scope of the concepts disclosed herein and the claims that follow. 

What is claimed is:
 1. A computer-implemented method comprising: receiving, from a device of a sending user, a message to be delivered to a subset of users in a select crowd of users; selecting one or more users from the users in the select crowd of users as the subset of the users in the select crowd of users to which the message is to be delivered; and sending the message to the one or more users selected as the subset of the users in the select crowd of users to which the message is to be delivered.
 2. The method of claim 1 wherein the select crowd of users is one of a plurality of dynamically defined crowds of users, and each crowd of the plurality of dynamically defined crowds of users includes a plurality of users that are spatially proximate to one another.
 3. The method of claim 1 wherein sending the message comprises sending the message anonymously such that the message does not identify the sending user.
 4. The method of claim 1 wherein selecting the one or more users comprises selecting the one or more users from the users in the select crowd of users as the subset of the users in the select crowd of users to which the message is to be delivered based on profile matching.
 5. The method of claim 4 wherein selecting the one or more users from the users in the select crowd of users as the subset of the users in the select crowd of users to which the message is to be delivered based on profile matching comprises: for each user of at least some of the users in the select crowd of users, generating a match value based on a comparison of at least a subset of a user profile of the sending user to at least a subset of a user profile of the user; and selecting the one or more users from the users in the select crowd of users as the subset of the users in the select crowd of users to which the message is to be delivered based on the match values of the at least some of the users in the select crowd of users.
 6. The method of claim 5 wherein the at least some of the users in the select crowd of users are all of the users in the select crowd of users.
 7. The method of claim 5 wherein the at least some of the users in the select crowd of users are one or more of the users in the select crowd of users that have enabled receipt of messages.
 8. The method of claim 5 wherein the user profile of the sending user comprises one or more profile categories, and the at least a subset of the user profile of the sending user is at least one profile category selected from the one or more profile categories.
 9. The method of claim 8 wherein, for each user of the at least some of the users in the select crowd of users, generating the match value comprises generating the match value for the user based on a comparison of the at least one profile category of the user profile of the sending user to at least a subset of the user profile of the user and at least one weight assigned to the at least one profile category.
 10. The method of claim 5 wherein selecting the one or more users from the users in the select crowd of users as the subset of the users in the select crowd of users to which the message is to be delivered based on the match values of the at least some of the users in the select crowd of users comprises: selecting a predefined number of users from the at least some of the users in the select crowd of users having the highest match values.
 11. The method of claim 5 wherein selecting the one or more users from the users in the select crowd of users as the subset of the users in the select crowd of users to which the message is to be delivered based on the match values of the at least some of the users in the select crowd of users comprises: selecting one or more users from the at least some of the users in the select crowd of users having match values that are greater than a defined minimum threshold score.
 12. The method of claim 4 wherein selecting the one or more users from the users in the select crowd of users as the subset of the users in the select crowd of users to which the message is to be delivered based on profile matching comprises: for each user of at least some of the users in the select crowd of users: generating a match value based on a comparison of at least a subset of a user profile of the sending user to at least a subset of a user profile of the user; and obtaining a referral rating for the user that is indicative of a desirability of the user as a recipient of the message; and selecting the one or more users from the users in the select crowd of users as the subset of the users in the select crowd of users to which the message is to be delivered based on the match values and the referral ratings of the at least some of the users in the select crowd of users.
 13. The method of claim 12 wherein the referral rating for the user is an implicit referral rating for the user.
 14. The method of claim 12 wherein the referral rating for the user is an explicit referral rating for the user.
 15. The method of claim 4 wherein selecting the one or more users from the users in the select crowd of users as the subset of the users in the select crowd of users to which the message is to be delivered based on profile matching comprises: for each user of at least some of the users in the select crowd of users: generating a match value based on a comparison of at least a subset of a user profile of the sending user to at least a subset of a user profile of the user; and obtaining a responsiveness rating for the user that is indicative of whether the user is likely to respond to the message from the sending user based on past actions of the user; and selecting the one or more users from the users in the select crowd of users as the subset of the users in the select crowd of users to which the message is to be delivered based on the match values and the responsiveness ratings of the at least some of the users in the select crowd of users.
 16. The method of claim 1 wherein selecting the one or more users comprises: for each user of at least some of the users in the select crowd of users, obtaining a referral rating for the user that is indicative of a desirability of the user as a recipient of the message; and selecting the one or more users from the users in the select crowd of users as the subset of the users in the select crowd of users to which the message is to be delivered based on the referral ratings of the at least some of the users in the select crowd of users.
 17. The method of claim 16 wherein the referral rating for the user is an implicit referral rating for the user.
 18. The method of claim 16 wherein the referral rating for the user is an explicit referral rating for the user.
 19. The method of claim 1 wherein selecting the one or more users comprises: for each user of at least some of the users in the select crowd of users, obtaining a responsiveness rating for the user that is indicative of a desirability of the user as a recipient of the message; and selecting the one or more users from the users in the select crowd of users as the subset of the users in the select crowd of users to which the message is to be delivered based on the responsiveness ratings of the at least some of the users in the select crowd of users.
 20. The method of claim 1 further comprising: receiving a response from a responding user from the one or more users to which the message was delivered; and sending the response to the sending user at the device of the sending user.
 21. The method of claim 20 wherein sending the response to the sending user comprises sending the response anonymously such that the response does not identify the responding user.
 22. The method of claim 20 further comprising: receiving, from the device of the sending user, a rating for the response assigned by the sending user; and updating an explicit referral rating of the responding user based on the rating for the response assigned by the sending user.
 23. The method of claim 20 further comprising: monitoring a location of the sending user to detect whether the sending user moves to a location of the select crowd of users; and updating an implicit referral rating of the responding user based on whether the sending user moves to the location of the select crowd of users.
 24. The method of claim 20 further comprising, for each user of the one or more users to which the message was delivered, updating a responsiveness rating of the user based on whether the user responded to the message.
 25. A server comprising: a communication interface; and a controller associated with the communication interface and adapted to: receive, from a device of a sending user, a message to be delivered to a subset of users in a select crowd of users; select one or more users from the users in the select crowd of users as the subset of the users in the select crowd of users to which the message is to be delivered; and send the message to the one or more users selected as the subset of the users in the select crowd of users to which the message is to be delivered.
 26. A computer readable medium storing software for instructing a controller of a computing device to: receive, from a device of a sending user, a message to be delivered to a subset of users in a select crowd of users; select one or more users from the users in the select crowd of users as the subset of the users in the select crowd of users to which the message is to be delivered; and send the message to the one or more users selected as the subset of the users in the select crowd of users to which the message is to be delivered. 